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A comprehensive literature review of the demand forecasting methods of emergency resources from the perspective of artificial intelligence

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  • Xiaoxin Zhu

    (Ocean University of China)

  • Guanghai Zhang

    (Ocean University of China)

  • Baiqing Sun

    (Harbin Institute of Technology)

Abstract

In recent decades, several forecasting methods have been proposed so as to aid in selecting from all optimal alternatives in the demand of emergency resources. Academic research in the field of emergency management has increasingly focused on artificial intelligence. However, more attention has been paid to attempts at simulating the human brain, with little focus on addressing intelligent information processing techniques based on machine learning, big data and smart devices. In this paper, a comprehensive literature review is presented in order to classify and interpret current research on demand forecasting methodologies and applications. A total of 1235 academic papers from 1980 to 2018 in the SpringerLink and Elsevier ScienceDirect databases are categorized as follows: time series analysis, case-based reasoning (CBR), mathematical models, information technology, literature reviews, and discussion and analysis. Application areas from business source premier include papers on the topics of emergency management, decision-making, decision relief, logistics, fuzzy sets and other topics. Academic publications are classified by (1) year of publication, (2) journal of publication, (3) database source, (4) methodology and (5) research discipline. The results of this literature review show that, despite forecasting methods such as ARIMA, CBR and mathematical models appearing to play a pivotal role in promoting prediction performance, there is a need to explore more real-time forecasting approaches based on intelligent information processing techniques so as to achieve appropriate dynamic demand prediction that is adaptable to emergency and rescue situations. The intention for this paper is to be a useful reference point for those with research needs in forecasting methodologies and the applications of emergency resources.

Suggested Citation

  • Xiaoxin Zhu & Guanghai Zhang & Baiqing Sun, 2019. "A comprehensive literature review of the demand forecasting methods of emergency resources from the perspective of artificial intelligence," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 97(1), pages 65-82, May.
    • Handle: RePEc:spr:nathaz:v:97:y:2019:i:1:d:10.1007_s11069-019-03626-z
    DOI: 10.1007/s11069-019-03626-z
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    1. Yossi Aviv, 2003. "A Time-Series Framework for Supply-Chain Inventory Management," Operations Research, INFORMS, vol. 51(2), pages 210-227, April.
    2. Kenneth Gilbert, 2005. "An ARIMA Supply Chain Model," Management Science, INFORMS, vol. 51(2), pages 305-310, February.
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    Cited by:

    1. Camila Pareja Yale & Hugo Tsugunobu Yoshida Yoshizaki & Luiz Paulo Fávero, 2022. "A New Zero-Inflated Negative Binomial Multilevel Model for Forecasting the Demand of Disaster Relief Supplies in the State of Sao Paulo, Brazil," Mathematics, MDPI, vol. 10(22), pages 1-11, November.
    2. Brielle Lillywhite & Gregor Wolbring, 2022. "Emergency and Disaster Management, Preparedness, and Planning (EDMPP) and the ‘Social’: A Scoping Review," Sustainability, MDPI, vol. 14(20), pages 1-50, October.
    3. Milad Zamanifar & Timo Hartmann, 2020. "Optimization-based decision-making models for disaster recovery and reconstruction planning of transportation networks," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(1), pages 1-25, October.
    4. Xiaoxin Zhu & David Regan & Baiqing Sun, 2022. "Does exploring the characteristics of emergency supplies really matter for disaster response operations?," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 110(1), pages 175-189, January.
    5. Shao, Jianfang & Liang, Changyong & Liu, Yujia & Xu, Jian & Zhao, Shuping, 2021. "Relief demand forecasting based on intuitionistic fuzzy case-based reasoning," Socio-Economic Planning Sciences, Elsevier, vol. 74(C).
    6. Xiaoxin Zhu & Yanyan Wang & David Regan & Baiqing Sun, 2020. "A Quantitative Study on Crucial Food Supplies after the 2011 Tohoku Earthquake Based on Time Series Analysis," IJERPH, MDPI, vol. 17(19), pages 1-13, September.
    7. Mandeep Kaur & Pankaj Deep Kaur & Sandeep Kumar Sood, 2021. "Energy efficient IoT-based cloud framework for early flood prediction," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 109(3), pages 2053-2076, December.

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